Economics at your fingertips  

Bayesian Assessment of Lorenz and Stochastic Dominance

David Lander, David Gunawan, William Griffiths () and Duangkamon Chotikapanich
Additional contact information
David Lander: Pennsylvania State University
David Gunawan: University of New South Wales
William Griffiths: Department of Economics, University of Melbourne,
Duangkamon Chotikapanich: Monash University

Department of Economics - Working Papers Series from The University of Melbourne

Abstract: Because of their applicability for ordering distributions within general classes of utility and social welfare functions, sampling theory tests for stochastic and Lorenz dominance have attracted considerable attention in the literature. We contribute to this literature by proposing a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo (MCMC), we compute posterior probabilities for (i) X dominates Y, (ii) Y dominates X, and (iii) neither Y nor X is dominant by counting the proportions of MCMC draws that satisfy the constraints implied by each of the alternatives. We apply the proposed approach to samples of Indonesian income distributions for 1999, 2002, 2005 and 2008. To ensure flexible modelling of the distributions, mixtures of gamma densities are fitted for each of the years. We introduce probability curves that depict the probability of dominance at each population proportion and which convey valuable information about dominance probabilities for restricted population proportions relevant when studying poverty orderings. The dominance probabilities are compared with p-values from some sampling theory tests; the probability curves are used to gain insights into seemingly contradictory outcomes

Keywords: Dominance probabilities; poverty comparisons; MCMC; gamma mixture. (search for similar items in EconPapers)
JEL-codes: C11 C12 D31 I32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ore, nep-sea and nep-upt
Date: 2017-03
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) ... nance13March2017.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Department of Economics - Working Papers Series from The University of Melbourne Department of Economics, The University of Melbourne, 4th Floor, FBE Building, Level 4, 111 Barry Street. Victoria, 3010, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Dandapani Lokanathan ().

Page updated 2019-09-16
Handle: RePEc:mlb:wpaper:2029